Overview

Brought to you by YData

Dataset statistics

Number of variables16
Number of observations7500
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory996.1 KiB
Average record size in memory136.0 B

Variable types

Numeric14
Categorical2

Alerts

churn is highly overall correlated with customer_service_callsHigh correlation
customer_service_calls is highly overall correlated with churnHigh correlation
churn is highly imbalanced (57.3%)Imbalance
total_eve_charge has unique valuesUnique
customer_hapiness has unique valuesUnique
n_sms has 561 (7.5%) zerosZeros
total_night_charge has 1995 (26.6%) zerosZeros
customer_service_calls has 3786 (50.5%) zerosZeros

Reproduction

Analysis started2024-08-31 19:56:05.742906
Analysis finished2024-08-31 19:56:18.750538
Duration13.01 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

area_code
Real number (ℝ)

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.0470667
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size117.2 KiB
2024-08-31T21:56:18.803143image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q37
95-th percentile9
Maximum9
Range8
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.5734285
Coefficient of variation (CV)0.50988598
Kurtosis-1.2201216
Mean5.0470667
Median Absolute Deviation (MAD)2
Skewness-0.016014202
Sum37853
Variance6.6225344
MonotonicityNot monotonic
2024-08-31T21:56:18.859238image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
6 867
11.6%
4 865
11.5%
9 864
11.5%
7 839
11.2%
8 827
11.0%
2 820
10.9%
3 818
10.9%
5 808
10.8%
1 792
10.6%
ValueCountFrequency (%)
1 792
10.6%
2 820
10.9%
3 818
10.9%
4 865
11.5%
5 808
10.8%
6 867
11.6%
7 839
11.2%
8 827
11.0%
9 864
11.5%
ValueCountFrequency (%)
9 864
11.5%
8 827
11.0%
7 839
11.2%
6 867
11.6%
5 808
10.8%
4 865
11.5%
3 818
10.9%
2 820
10.9%
1 792
10.6%

plan
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size117.2 KiB
2.0
2515 
3.0
2508 
1.0
2477 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters22500
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row3.0
3rd row1.0
4th row1.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 2515
33.5%
3.0 2508
33.4%
1.0 2477
33.0%

Length

2024-08-31T21:56:18.924383image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-31T21:56:18.988081image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
2.0 2515
33.5%
3.0 2508
33.4%
1.0 2477
33.0%

Most occurring characters

ValueCountFrequency (%)
. 7500
33.3%
0 7500
33.3%
2 2515
 
11.2%
3 2508
 
11.1%
1 2477
 
11.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 22500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 7500
33.3%
0 7500
33.3%
2 2515
 
11.2%
3 2508
 
11.1%
1 2477
 
11.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 22500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 7500
33.3%
0 7500
33.3%
2 2515
 
11.2%
3 2508
 
11.1%
1 2477
 
11.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 22500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 7500
33.3%
0 7500
33.3%
2 2515
 
11.2%
3 2508
 
11.1%
1 2477
 
11.0%

n_sms
Real number (ℝ)

ZEROS 

Distinct822
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean306.74547
Minimum0
Maximum1070
Zeros561
Zeros (%)7.5%
Negative0
Negative (%)0.0%
Memory size117.2 KiB
2024-08-31T21:56:19.053951image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1158
median298
Q3441
95-th percentile645.05
Maximum1070
Range1070
Interquartile range (IQR)283

Descriptive statistics

Standard deviation196.35166
Coefficient of variation (CV)0.64011267
Kurtosis-0.3537701
Mean306.74547
Median Absolute Deviation (MAD)141
Skewness0.35025717
Sum2300591
Variance38553.974
MonotonicityNot monotonic
2024-08-31T21:56:19.126711image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 561
 
7.5%
244 24
 
0.3%
280 24
 
0.3%
469 22
 
0.3%
361 22
 
0.3%
308 22
 
0.3%
229 21
 
0.3%
287 21
 
0.3%
228 21
 
0.3%
230 21
 
0.3%
Other values (812) 6741
89.9%
ValueCountFrequency (%)
0 561
7.5%
1 9
 
0.1%
2 1
 
< 0.1%
3 6
 
0.1%
4 2
 
< 0.1%
5 6
 
0.1%
6 4
 
0.1%
7 3
 
< 0.1%
8 6
 
0.1%
9 8
 
0.1%
ValueCountFrequency (%)
1070 1
< 0.1%
1022 2
< 0.1%
1020 1
< 0.1%
1019 1
< 0.1%
999 1
< 0.1%
972 1
< 0.1%
967 1
< 0.1%
965 1
< 0.1%
957 1
< 0.1%
946 1
< 0.1%

total_day_minutes
Real number (ℝ)

Distinct7450
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean904.61862
Minimum0
Maximum2200.3418
Zeros51
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size117.2 KiB
2024-08-31T21:56:19.188825image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile299.70013
Q1657.99236
median904.56577
Q31145.765
95-th percentile1508.2638
Maximum2200.3418
Range2200.3418
Interquartile range (IQR)487.77261

Descriptive statistics

Standard deviation361.02498
Coefficient of variation (CV)0.39909082
Kurtosis-0.1263627
Mean904.61862
Median Absolute Deviation (MAD)243.52095
Skewness0.041675731
Sum6784639.7
Variance130339.04
MonotonicityNot monotonic
2024-08-31T21:56:19.257258image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 51
 
0.7%
869.0255343 1
 
< 0.1%
641.5913782 1
 
< 0.1%
736.3438788 1
 
< 0.1%
1122.480966 1
 
< 0.1%
911.6610315 1
 
< 0.1%
758.1782017 1
 
< 0.1%
780.6994763 1
 
< 0.1%
549.5423187 1
 
< 0.1%
981.6882678 1
 
< 0.1%
Other values (7440) 7440
99.2%
ValueCountFrequency (%)
0 51
0.7%
0.03825805909 1
 
< 0.1%
1.338827963 1
 
< 0.1%
5.661380733 1
 
< 0.1%
8.374701903 1
 
< 0.1%
8.976510403 1
 
< 0.1%
10.15815743 1
 
< 0.1%
16.42871301 1
 
< 0.1%
17.09916813 1
 
< 0.1%
20.22175266 1
 
< 0.1%
ValueCountFrequency (%)
2200.341761 1
< 0.1%
2127.189447 1
< 0.1%
2122.829453 1
< 0.1%
2105.497497 1
< 0.1%
2103.098407 1
< 0.1%
2082.04577 1
< 0.1%
2082.024846 1
< 0.1%
2028.959562 1
< 0.1%
2015.444121 1
< 0.1%
2012.247058 1
< 0.1%

total_day_calls
Real number (ℝ)

Distinct330
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean150.60107
Minimum0
Maximum425
Zeros49
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size117.2 KiB
2024-08-31T21:56:19.320414image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile52
Q1110
median151
Q3190
95-th percentile250
Maximum425
Range425
Interquartile range (IQR)80

Descriptive statistics

Standard deviation59.712375
Coefficient of variation (CV)0.39649371
Kurtosis-0.050081007
Mean150.60107
Median Absolute Deviation (MAD)40
Skewness0.061042561
Sum1129508
Variance3565.5677
MonotonicityNot monotonic
2024-08-31T21:56:19.382003image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
161 64
 
0.9%
159 61
 
0.8%
148 60
 
0.8%
180 60
 
0.8%
168 59
 
0.8%
140 58
 
0.8%
125 57
 
0.8%
147 57
 
0.8%
135 55
 
0.7%
164 55
 
0.7%
Other values (320) 6914
92.2%
ValueCountFrequency (%)
0 49
0.7%
1 3
 
< 0.1%
2 1
 
< 0.1%
3 3
 
< 0.1%
4 1
 
< 0.1%
5 3
 
< 0.1%
6 2
 
< 0.1%
8 2
 
< 0.1%
9 4
 
0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
425 1
 
< 0.1%
367 1
 
< 0.1%
358 1
 
< 0.1%
355 3
< 0.1%
351 1
 
< 0.1%
345 1
 
< 0.1%
340 1
 
< 0.1%
339 1
 
< 0.1%
338 1
 
< 0.1%
336 1
 
< 0.1%

total_day_charge
Real number (ℝ)

Distinct7494
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.675844
Minimum0
Maximum98.408197
Zeros7
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size117.2 KiB
2024-08-31T21:56:19.445069image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile19.558587
Q134.659855
median44.717577
Q354.731811
95-th percentile69.056084
Maximum98.408197
Range98.408197
Interquartile range (IQR)20.071956

Descriptive statistics

Standard deviation15.043472
Coefficient of variation (CV)0.33672496
Kurtosis-0.042983716
Mean44.675844
Median Absolute Deviation (MAD)10.034248
Skewness0.026005947
Sum335068.83
Variance226.30604
MonotonicityNot monotonic
2024-08-31T21:56:19.508573image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7
 
0.1%
50.44969101 1
 
< 0.1%
38.72112118 1
 
< 0.1%
52.73357609 1
 
< 0.1%
52.71349676 1
 
< 0.1%
43.39498404 1
 
< 0.1%
46.02435162 1
 
< 0.1%
63.86359274 1
 
< 0.1%
54.79469604 1
 
< 0.1%
48.81811409 1
 
< 0.1%
Other values (7484) 7484
99.8%
ValueCountFrequency (%)
0 7
0.1%
0.9287915823 1
 
< 0.1%
1.022292004 1
 
< 0.1%
1.126543757 1
 
< 0.1%
2.219584772 1
 
< 0.1%
2.707283264 1
 
< 0.1%
2.867761919 1
 
< 0.1%
3.188209603 1
 
< 0.1%
3.765365939 1
 
< 0.1%
3.766806931 1
 
< 0.1%
ValueCountFrequency (%)
98.40819671 1
< 0.1%
98.32200589 1
< 0.1%
97.27460884 1
< 0.1%
95.33499891 1
< 0.1%
94.73419765 1
< 0.1%
93.79299949 1
< 0.1%
93.75503867 1
< 0.1%
93.06398622 1
< 0.1%
92.62104596 1
< 0.1%
92.5660562 1
< 0.1%

total_eve_minutes
Real number (ℝ)

Distinct7484
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean599.01458
Minimum0
Maximum1440.7829
Zeros17
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size117.2 KiB
2024-08-31T21:56:19.568743image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile256.09425
Q1459.19789
median597.72644
Q3739.24961
95-th percentile945.87888
Maximum1440.7829
Range1440.7829
Interquartile range (IQR)280.05173

Descriptive statistics

Standard deviation209.53753
Coefficient of variation (CV)0.34980373
Kurtosis0.038091193
Mean599.01458
Median Absolute Deviation (MAD)140.07486
Skewness0.058362743
Sum4492609.3
Variance43905.978
MonotonicityNot monotonic
2024-08-31T21:56:19.632693image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 17
 
0.2%
681.643301 1
 
< 0.1%
717.7364393 1
 
< 0.1%
638.6540726 1
 
< 0.1%
424.994423 1
 
< 0.1%
226.3672427 1
 
< 0.1%
575.9321347 1
 
< 0.1%
626.9059039 1
 
< 0.1%
368.6109878 1
 
< 0.1%
565.2745296 1
 
< 0.1%
Other values (7474) 7474
99.7%
ValueCountFrequency (%)
0 17
0.2%
0.1377191373 1
 
< 0.1%
9.504674031 1
 
< 0.1%
10.02429358 1
 
< 0.1%
12.81616532 1
 
< 0.1%
15.71273512 1
 
< 0.1%
17.53084078 1
 
< 0.1%
20.04548112 1
 
< 0.1%
21.28808554 1
 
< 0.1%
26.29539222 1
 
< 0.1%
ValueCountFrequency (%)
1440.782944 1
< 0.1%
1387.196936 1
< 0.1%
1386.625588 1
< 0.1%
1382.942128 1
< 0.1%
1368.544899 1
< 0.1%
1313.26085 1
< 0.1%
1308.243583 1
< 0.1%
1288.966776 1
< 0.1%
1286.978616 1
< 0.1%
1278.38289 1
< 0.1%

total_eve_calls
Real number (ℝ)

Distinct324
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean149.52947
Minimum0
Maximum386
Zeros48
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size117.2 KiB
2024-08-31T21:56:19.697591image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile50
Q1110
median150
Q3190
95-th percentile246
Maximum386
Range386
Interquartile range (IQR)80

Descriptive statistics

Standard deviation59.039
Coefficient of variation (CV)0.39483187
Kurtosis-0.10287171
Mean149.52947
Median Absolute Deviation (MAD)40
Skewness0.024770902
Sum1121471
Variance3485.6035
MonotonicityNot monotonic
2024-08-31T21:56:19.763619image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
144 60
 
0.8%
190 59
 
0.8%
118 58
 
0.8%
126 57
 
0.8%
177 57
 
0.8%
165 57
 
0.8%
159 55
 
0.7%
171 55
 
0.7%
155 55
 
0.7%
158 55
 
0.7%
Other values (314) 6932
92.4%
ValueCountFrequency (%)
0 48
0.6%
1 1
 
< 0.1%
2 1
 
< 0.1%
4 4
 
0.1%
5 1
 
< 0.1%
6 3
 
< 0.1%
7 4
 
0.1%
8 7
 
0.1%
9 3
 
< 0.1%
10 3
 
< 0.1%
ValueCountFrequency (%)
386 1
< 0.1%
372 1
< 0.1%
359 1
< 0.1%
356 1
< 0.1%
352 1
< 0.1%
346 2
< 0.1%
345 1
< 0.1%
338 1
< 0.1%
336 1
< 0.1%
334 2
< 0.1%

total_eve_charge
Real number (ℝ)

UNIQUE 

Distinct7500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.845465
Minimum0
Maximum63.571876
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size117.2 KiB
2024-08-31T21:56:19.829304image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile14.853029
Q123.742752
median29.952025
Q335.993937
95-th percentile44.132718
Maximum63.571876
Range63.571876
Interquartile range (IQR)12.251185

Descriptive statistics

Standard deviation8.9443674
Coefficient of variation (CV)0.29968933
Kurtosis0.032734748
Mean29.845465
Median Absolute Deviation (MAD)6.1165394
Skewness-0.026420325
Sum223840.99
Variance80.001708
MonotonicityNot monotonic
2024-08-31T21:56:19.891128image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.12269024 1
 
< 0.1%
34.88206251 1
 
< 0.1%
35.47041119 1
 
< 0.1%
21.97347598 1
 
< 0.1%
23.02480257 1
 
< 0.1%
29.37376598 1
 
< 0.1%
29.79987972 1
 
< 0.1%
29.50761315 1
 
< 0.1%
24.72363852 1
 
< 0.1%
28.66744893 1
 
< 0.1%
Other values (7490) 7490
99.9%
ValueCountFrequency (%)
0 1
< 0.1%
2.077529726 1
< 0.1%
2.1237108 1
< 0.1%
2.42799762 1
< 0.1%
2.460518018 1
< 0.1%
2.482991654 1
< 0.1%
2.614324291 1
< 0.1%
2.665484773 1
< 0.1%
2.730636091 1
< 0.1%
2.74017399 1
< 0.1%
ValueCountFrequency (%)
63.57187562 1
< 0.1%
63.37683574 1
< 0.1%
61.28838206 1
< 0.1%
60.94966195 1
< 0.1%
60.32225723 1
< 0.1%
59.70201038 1
< 0.1%
59.53158352 1
< 0.1%
59.1749612 1
< 0.1%
58.81369383 1
< 0.1%
58.76784239 1
< 0.1%

total_night_minutes
Real number (ℝ)

Distinct7496
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean210.33066
Minimum0
Maximum445.63189
Zeros5
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size117.2 KiB
2024-08-31T21:56:19.954830image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile112.9553
Q1169.81354
median209.8147
Q3249.82276
95-th percentile308.01656
Maximum445.63189
Range445.63189
Interquartile range (IQR)80.009219

Descriptive statistics

Standard deviation59.186122
Coefficient of variation (CV)0.28139561
Kurtosis0.044379532
Mean210.33066
Median Absolute Deviation (MAD)40.008812
Skewness0.0031829705
Sum1577479.9
Variance3502.9971
MonotonicityNot monotonic
2024-08-31T21:56:20.015420image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5
 
0.1%
157.6391977 1
 
< 0.1%
170.7245347 1
 
< 0.1%
221.5839565 1
 
< 0.1%
130.0367792 1
 
< 0.1%
296.5277913 1
 
< 0.1%
187.7887801 1
 
< 0.1%
240.3317383 1
 
< 0.1%
127.6229598 1
 
< 0.1%
230.189301 1
 
< 0.1%
Other values (7486) 7486
99.8%
ValueCountFrequency (%)
0 5
0.1%
0.5990210512 1
 
< 0.1%
7.126416466 1
 
< 0.1%
15.80651178 1
 
< 0.1%
26.01053946 1
 
< 0.1%
28.49409364 1
 
< 0.1%
33.7034836 1
 
< 0.1%
36.03123731 1
 
< 0.1%
37.36409508 1
 
< 0.1%
37.55282617 1
 
< 0.1%
ValueCountFrequency (%)
445.6318941 1
< 0.1%
425.2801296 1
< 0.1%
422.5045521 1
< 0.1%
412.6554767 1
< 0.1%
401.6885242 1
< 0.1%
401.0082237 1
< 0.1%
396.7271193 1
< 0.1%
395.731357 1
< 0.1%
391.8204284 1
< 0.1%
391.4260794 1
< 0.1%

total_night_calls
Real number (ℝ)

Distinct131
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59.4808
Minimum0
Maximum135
Zeros11
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size117.2 KiB
2024-08-31T21:56:20.076065image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile25
Q145
median59
Q374
95-th percentile93
Maximum135
Range135
Interquartile range (IQR)29

Descriptive statistics

Standard deviation20.86939
Coefficient of variation (CV)0.35085927
Kurtosis-0.038913481
Mean59.4808
Median Absolute Deviation (MAD)14
Skewness0.042417758
Sum446106
Variance435.53144
MonotonicityNot monotonic
2024-08-31T21:56:20.142689image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
58 161
 
2.1%
55 160
 
2.1%
62 156
 
2.1%
57 150
 
2.0%
53 148
 
2.0%
51 146
 
1.9%
54 144
 
1.9%
70 142
 
1.9%
63 139
 
1.9%
66 137
 
1.8%
Other values (121) 6017
80.2%
ValueCountFrequency (%)
0 11
0.1%
1 4
 
0.1%
2 3
 
< 0.1%
3 1
 
< 0.1%
4 4
 
0.1%
5 5
0.1%
6 5
0.1%
7 6
0.1%
8 4
 
0.1%
9 2
 
< 0.1%
ValueCountFrequency (%)
135 1
 
< 0.1%
134 3
< 0.1%
132 2
 
< 0.1%
131 1
 
< 0.1%
129 2
 
< 0.1%
126 2
 
< 0.1%
125 1
 
< 0.1%
124 1
 
< 0.1%
123 6
0.1%
121 1
 
< 0.1%

total_night_charge
Real number (ℝ)

ZEROS 

Distinct5506
Distinct (%)73.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.94978
Minimum0
Maximum106.65388
Zeros1995
Zeros (%)26.6%
Negative0
Negative (%)0.0%
Memory size117.2 KiB
2024-08-31T21:56:20.207157image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median15.17492
Q331.053053
95-th percentile54.627117
Maximum106.65388
Range106.65388
Interquartile range (IQR)31.053053

Descriptive statistics

Standard deviation18.756229
Coefficient of variation (CV)0.98978607
Kurtosis0.29141633
Mean18.94978
Median Absolute Deviation (MAD)15.17492
Skewness0.9054566
Sum142123.35
Variance351.79611
MonotonicityNot monotonic
2024-08-31T21:56:20.270595image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1995
 
26.6%
25.1639883 1
 
< 0.1%
23.89525258 1
 
< 0.1%
0.1754112002 1
 
< 0.1%
25.02263682 1
 
< 0.1%
27.09354147 1
 
< 0.1%
9.825129309 1
 
< 0.1%
54.75293129 1
 
< 0.1%
40.34505966 1
 
< 0.1%
26.04051897 1
 
< 0.1%
Other values (5496) 5496
73.3%
ValueCountFrequency (%)
0 1995
26.6%
0.000806207892 1
 
< 0.1%
0.002440423866 1
 
< 0.1%
0.004565472541 1
 
< 0.1%
0.01474158119 1
 
< 0.1%
0.02807394595 1
 
< 0.1%
0.03002501315 1
 
< 0.1%
0.03060909829 1
 
< 0.1%
0.06025733014 1
 
< 0.1%
0.07308376074 1
 
< 0.1%
ValueCountFrequency (%)
106.65388 1
< 0.1%
105.1705078 1
< 0.1%
100.8124047 1
< 0.1%
97.28332675 1
< 0.1%
93.14338367 1
< 0.1%
92.94639622 1
< 0.1%
92.53850748 1
< 0.1%
91.84871345 1
< 0.1%
91.8454027 1
< 0.1%
90.4918436 1
< 0.1%

customer_service_calls
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct95
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.841067
Minimum0
Maximum111
Zeros3786
Zeros (%)50.5%
Negative0
Negative (%)0.0%
Memory size117.2 KiB
2024-08-31T21:56:20.333424image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q320
95-th percentile49
Maximum111
Range111
Interquartile range (IQR)20

Descriptive statistics

Standard deviation17.351702
Coefficient of variation (CV)1.4653833
Kurtosis2.3370346
Mean11.841067
Median Absolute Deviation (MAD)0
Skewness1.6303782
Sum88808
Variance301.08155
MonotonicityNot monotonic
2024-08-31T21:56:20.394444image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3786
50.5%
1 129
 
1.7%
6 115
 
1.5%
3 113
 
1.5%
5 110
 
1.5%
2 107
 
1.4%
15 103
 
1.4%
8 102
 
1.4%
23 95
 
1.3%
7 92
 
1.2%
Other values (85) 2748
36.6%
ValueCountFrequency (%)
0 3786
50.5%
1 129
 
1.7%
2 107
 
1.4%
3 113
 
1.5%
4 78
 
1.0%
5 110
 
1.5%
6 115
 
1.5%
7 92
 
1.2%
8 102
 
1.4%
9 80
 
1.1%
ValueCountFrequency (%)
111 1
 
< 0.1%
104 1
 
< 0.1%
98 1
 
< 0.1%
96 1
 
< 0.1%
94 1
 
< 0.1%
92 2
< 0.1%
91 2
< 0.1%
90 3
< 0.1%
88 3
< 0.1%
87 4
0.1%

customer_service_rating
Real number (ℝ)

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.3952
Minimum0
Maximum10
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size117.2 KiB
2024-08-31T21:56:20.447842image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q16
median8
Q39
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.8752473
Coefficient of variation (CV)0.25357628
Kurtosis-0.29631591
Mean7.3952
Median Absolute Deviation (MAD)1
Skewness-0.4611801
Sum55464
Variance3.5165525
MonotonicityNot monotonic
2024-08-31T21:56:20.497782image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
8 1436
19.1%
7 1380
18.4%
9 1192
15.9%
10 1170
15.6%
6 1096
14.6%
5 671
8.9%
4 366
 
4.9%
3 134
 
1.8%
2 39
 
0.5%
1 13
 
0.2%
ValueCountFrequency (%)
0 3
 
< 0.1%
1 13
 
0.2%
2 39
 
0.5%
3 134
 
1.8%
4 366
 
4.9%
5 671
8.9%
6 1096
14.6%
7 1380
18.4%
8 1436
19.1%
9 1192
15.9%
ValueCountFrequency (%)
10 1170
15.6%
9 1192
15.9%
8 1436
19.1%
7 1380
18.4%
6 1096
14.6%
5 671
8.9%
4 366
 
4.9%
3 134
 
1.8%
2 39
 
0.5%
1 13
 
0.2%

customer_hapiness
Real number (ℝ)

UNIQUE 

Distinct7500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.50245275
Minimum0.00017939101
Maximum0.99986871
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size117.2 KiB
2024-08-31T21:56:20.684640image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.00017939101
5-th percentile0.051810835
Q10.25056444
median0.50786818
Q30.75257533
95-th percentile0.95064888
Maximum0.99986871
Range0.99968932
Interquartile range (IQR)0.50201089

Descriptive statistics

Standard deviation0.28985428
Coefficient of variation (CV)0.57687868
Kurtosis-1.2200514
Mean0.50245275
Median Absolute Deviation (MAD)0.25176169
Skewness-0.007159829
Sum3768.3956
Variance0.084015503
MonotonicityNot monotonic
2024-08-31T21:56:20.750156image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2982344468 1
 
< 0.1%
0.908372139 1
 
< 0.1%
0.08111243983 1
 
< 0.1%
0.9225696282 1
 
< 0.1%
0.5810198802 1
 
< 0.1%
0.05552150173 1
 
< 0.1%
0.9530781173 1
 
< 0.1%
0.3772880793 1
 
< 0.1%
0.4053539076 1
 
< 0.1%
0.3602364119 1
 
< 0.1%
Other values (7490) 7490
99.9%
ValueCountFrequency (%)
0.0001793910098 1
< 0.1%
0.0002135787151 1
< 0.1%
0.0004282800256 1
< 0.1%
0.0006435799683 1
< 0.1%
0.0007028449389 1
< 0.1%
0.0007159080322 1
< 0.1%
0.0008807913954 1
< 0.1%
0.001564124626 1
< 0.1%
0.001667041497 1
< 0.1%
0.001753145611 1
< 0.1%
ValueCountFrequency (%)
0.9998687081 1
< 0.1%
0.9997737425 1
< 0.1%
0.9995118717 1
< 0.1%
0.9992886641 1
< 0.1%
0.9991603916 1
< 0.1%
0.9991286197 1
< 0.1%
0.9990361676 1
< 0.1%
0.9990232501 1
< 0.1%
0.9989040662 1
< 0.1%
0.9987561297 1
< 0.1%

churn
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size117.2 KiB
0
6847 
1
 
653

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 6847
91.3%
1 653
 
8.7%

Length

2024-08-31T21:56:20.808157image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-31T21:56:20.854371image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 6847
91.3%
1 653
 
8.7%

Most occurring characters

ValueCountFrequency (%)
0 6847
91.3%
1 653
 
8.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 6847
91.3%
1 653
 
8.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 6847
91.3%
1 653
 
8.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 6847
91.3%
1 653
 
8.7%

Interactions

2024-08-31T21:56:17.519620image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:06.101995image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:07.108046image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:08.012152image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:08.872270image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:09.773100image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:10.483894image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:11.254411image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:12.091115image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:12.788613image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:13.860882image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:14.936063image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:15.726090image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:16.540723image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:17.575733image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:06.188701image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:07.172141image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:08.074054image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:08.925446image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:09.823053image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:10.534540image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:11.306982image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:12.144876image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:12.837421image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:13.924366image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:14.988028image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:15.785256image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:16.599756image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:17.640211image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:06.314038image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:07.308609image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:08.126331image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:09.042804image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:09.867889image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:10.582056image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:11.354633image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:12.194257image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:12.882411image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:13.980087image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:15.035509image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:15.839838image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:16.656686image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:17.711071image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:06.399399image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:07.385257image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:08.185695image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:09.092769image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:09.948685image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:10.627098image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:11.404157image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:12.242086image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:12.967303image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:14.036577image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:15.080582image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:15.891549image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:16.708272image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:17.821852image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:06.449656image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:07.433965image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:08.234448image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:09.177902image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:10.000672image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:10.675030image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:11.518064image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:12.287195image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:13.027243image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:14.092814image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:15.166931image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:15.942613image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:16.760432image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:17.928771image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:06.501798image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:07.480136image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:08.286002image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:09.237489image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:10.049714image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:10.723812image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:11.576317image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:12.336259image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:13.101393image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:14.160020image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:15.214854image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:15.996570image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:16.817705image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:18.006547image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:06.567304image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:07.530448image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:08.361872image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:09.311384image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:10.100063image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:10.776906image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:11.644384image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:12.388475image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:13.175769image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:14.244822image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:15.265028image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:16.055897image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:16.882200image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:18.099895image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:06.642253image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:07.593960image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:08.445665image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:09.384907image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:10.160205image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:10.830339image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:11.721390image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:12.443589image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:13.239899image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:14.319160image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:15.321029image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:16.112355image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:16.993834image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:18.154317image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:06.704163image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:07.659960image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:08.509854image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:09.453019image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:10.209150image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:10.881410image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:11.789570image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:12.491751image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:13.293837image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:14.411927image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:15.380599image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:16.220745image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:17.182208image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:18.208546image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:06.758858image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:07.757495image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:08.568168image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:09.510682image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:10.253576image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:10.929118image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:11.837992image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:12.540438image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:13.376976image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:14.507717image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:15.441306image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:16.275007image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:17.233317image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:18.264447image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:06.818357image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:07.811607image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:08.621292image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:09.572883image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:10.301949image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:11.059834image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:11.891729image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:12.594281image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:13.442765image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:14.686592image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:15.501835image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:16.336514image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:17.300064image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:18.319757image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:06.873546image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:07.860301image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:08.674227image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:09.620945image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:10.346672image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:11.109001image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:11.940989image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:12.642466image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:13.692664image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:14.778690image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:15.561674image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:16.388252image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:17.356287image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:18.370526image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:06.935643image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:07.909838image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:08.729052image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:09.668963image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:10.391173image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:11.158542image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:11.991281image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:12.690733image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:13.746697image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:14.830437image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:15.617410image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:16.442437image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:17.410709image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:18.423400image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:06.987462image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:07.960899image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:08.790764image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:09.724073image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:10.437446image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:11.206078image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:12.041102image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:12.739748image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:13.802247image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:14.883438image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:15.669495image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:16.489772image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-31T21:56:17.465041image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-08-31T21:56:20.894515image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
area_codechurncustomer_hapinesscustomer_service_callscustomer_service_ratingn_smsplantotal_day_callstotal_day_chargetotal_day_minutestotal_eve_callstotal_eve_chargetotal_eve_minutestotal_night_callstotal_night_chargetotal_night_minutes
area_code1.0000.0000.0120.0000.0010.0210.000-0.010-0.0070.0140.0100.013-0.0200.008-0.0140.001
churn0.0001.0000.2510.5790.2130.0190.0000.0270.0000.0190.0000.0360.0000.0150.0000.038
customer_hapiness0.0120.2511.000-0.007-0.027-0.0060.000-0.0320.0030.0160.0060.0020.0030.0230.0160.010
customer_service_calls0.0000.579-0.0071.0000.0290.0170.0170.0070.0180.0020.008-0.000-0.0090.019-0.005-0.007
customer_service_rating0.0010.213-0.0270.0291.0000.0140.0230.013-0.002-0.011-0.001-0.022-0.0290.012-0.017-0.023
n_sms0.0210.019-0.0060.0170.0141.0000.0160.0070.0110.0290.0070.0220.004-0.0090.000-0.009
plan0.0000.0000.0000.0170.0230.0161.0000.0050.0000.0000.0000.0000.0270.0000.0100.000
total_day_calls-0.0100.027-0.0320.0070.0130.0070.0051.0000.0090.003-0.001-0.0190.0040.0000.0080.001
total_day_charge-0.0070.0000.0030.018-0.0020.0110.0000.0091.0000.025-0.029-0.006-0.004-0.002-0.003-0.004
total_day_minutes0.0140.0190.0160.002-0.0110.0290.0000.0030.0251.000-0.015-0.004-0.0020.009-0.0130.019
total_eve_calls0.0100.0000.0060.008-0.0010.0070.000-0.001-0.029-0.0151.0000.0150.0030.0130.015-0.014
total_eve_charge0.0130.0360.002-0.000-0.0220.0220.000-0.019-0.006-0.0040.0151.0000.001-0.016-0.004-0.005
total_eve_minutes-0.0200.0000.003-0.009-0.0290.0040.0270.004-0.004-0.0020.0030.0011.000-0.0080.002-0.005
total_night_calls0.0080.0150.0230.0190.012-0.0090.0000.000-0.0020.0090.013-0.016-0.0081.000-0.011-0.021
total_night_charge-0.0140.0000.016-0.005-0.0170.0000.0100.008-0.003-0.0130.015-0.0040.002-0.0111.000-0.009
total_night_minutes0.0010.0380.010-0.007-0.023-0.0090.0000.001-0.0040.019-0.014-0.005-0.005-0.021-0.0091.000

Missing values

2024-08-31T21:56:18.500762image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-08-31T21:56:18.628452image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

area_codeplann_smstotal_day_minutestotal_day_callstotal_day_chargetotal_eve_minutestotal_eve_callstotal_eve_chargetotal_night_minutestotal_night_callstotal_night_chargecustomer_service_callscustomer_service_ratingcustomer_hapinesschurn
2375225.02.07241365.99102120350.449691681.64330114033.122690157.6391985325.1639881480.2982340
8472762.03.03871253.39439715877.050620437.9415338820.629900220.1590293258.178678080.4247400
2424508.01.0490627.68709916542.508170618.2319705417.826781178.2980048547.7851263250.3788051
3772213.01.0822601.81633311572.020707605.25575910627.550356212.695526306.7652522590.1750850
9915061.02.0455951.01971514044.885685320.5387437525.209541217.3640119825.8026693660.6126070
4282179.03.0644893.82061824744.661809507.34914715122.746932333.763431640.000000070.9692920
3271726.02.05941210.17825816938.250627735.5235981721.219536195.102671785.3458710100.2620180
9305967.01.0133365.90661023143.589122234.331968031.366369144.1802647012.260392080.7984520
4240363.03.0102126.487324047.356321601.6192854427.590046261.7825309426.7664780100.9992890
274911.02.0442409.24588419558.906716803.47925518730.507035168.9402376240.123818050.4591320
area_codeplann_smstotal_day_minutestotal_day_callstotal_day_chargetotal_eve_minutestotal_eve_callstotal_eve_chargetotal_night_minutestotal_night_callstotal_night_chargecustomer_service_callscustomer_service_ratingcustomer_hapinesschurn
8110727.02.02151570.6230429542.614391644.44444712737.524457189.5667936517.605139050.9544990
7900468.02.0372297.44419626841.917049742.99823921038.655664102.3505595632.416889050.7382420
6270357.02.03510.00000014665.703691899.92103914739.87078789.733695475.347156050.2960360
5413072.02.066473.78920617646.610144306.00367023027.491096187.5163453753.263817040.8983820
961707.01.0327972.66535126444.698007817.31463011152.858449106.6364935668.154185270.9652970
1930922.03.01011611.34060020857.553839671.93265919231.206479257.6266705435.793522090.9372420
8525063.01.0362740.22624315938.9385971027.75431517825.844785282.9562086522.2531934170.4700760
7450633.03.00822.69106520161.122407117.55626211445.922274149.3166565511.10582929100.0092051
9830448.01.0470708.9880282539.954708396.34331421821.334555164.6051661170.000000070.7407390
7947458.01.0518769.40917716746.901915505.9232951536.512523260.8564726737.9387941680.9070850